{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LLM reasoning with DeepSeek-R1 distilled models\n",
    "\n",
    "[DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf) is an open-source reasoning model developed by DeepSeek to address tasks requiring logical inference, mathematical problem-solving, and real-time decision-making. With DeepSeek-R1, you can follow its logic, making it easier to understand and, if necessary, challenge its output. This capability gives reasoning models an edge in fields where outcomes need to be explainable, like research or complex decision-making.\n",
    "\n",
    "Distillation in AI creates smaller, more efficient models from larger ones, preserving much of their reasoning power while reducing computational demands. DeepSeek applied this technique to create a suite of distilled models from R1, using Qwen and Llama architectures. That allows us to try DeepSeek-R1 capability locally on usual laptops.\n",
    "\n",
    "In this tutorial, we consider how to run DeepSeek-R1 distilled models using OpenVINO.\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Prerequisites](#Prerequisites)\n",
    "- [Select model for inference](#Select-model-for-inference)\n",
    "- [Convert model using Optimum-CLI tool](#Convert-model-using-Optimum-CLI-tool)\n",
    "    - [Weights Compression using Optimum-CLI](#Weights-Compression-using-Optimum-CLI)\n",
    "- [Instantiate pipeline with OpenVINO Generate API](#Instantiate-pipeline-with-OpenVINO-Generate-API)\n",
    "- [Run Chatbot](#Run-Chatbot)\n",
    "\n",
    "\n",
    "### Installation Instructions\n",
    "\n",
    "This is a self-contained example that relies solely on its own code.\n",
    "\n",
    "We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.\n",
    "For details, please refer to [Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide).\n",
    "\n",
    "<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/deepseek-r1/deepseek-r1.ipynb\" />\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "Install required dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import platform\n",
    "\n",
    "os.environ[\"GIT_CLONE_PROTECTION_ACTIVE\"] = \"false\"\n",
    "\n",
    "%pip install -q -U --pre \"openvino>=2024.6.0\" openvino-tokenizers openvino-genai --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly\n",
    "%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\\\n",
    "\"git+https://github.com/huggingface/optimum-intel.git\"\\\n",
    "\"nncf==2.14.1\"\\\n",
    "\"torch==2.8\"\\\n",
    "\"datasets<4.0.0\" \\\n",
    "\"accelerate\" \\\n",
    "\"gradio>=4.19,<6\" \\\n",
    "\"transformers==4.53.3\" \\\n",
    "\"huggingface-hub>=0.26.5\" \\\n",
    "\"einops\" \"tiktoken\"\n",
    "\n",
    "if platform.system() == \"Darwin\":\n",
    "    %pip install -q \"numpy<2.0.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/openvino/runtime/__init__.py:10: DeprecationWarning: The `openvino.runtime` module is deprecated and will be removed in the 2026.0 release. Please replace `openvino.runtime` with `openvino`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path\n",
    "\n",
    "if not Path(\"llm_config.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\")\n",
    "    open(\"llm_config.py\", \"w\").write(r.text)\n",
    "\n",
    "if not Path(\"notebook_utils.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\")\n",
    "    open(\"notebook_utils.py\", \"w\").write(r.text)\n",
    "\n",
    "if not Path(\"genai_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/genai_helper.py\")\n",
    "    open(\"genai_helper.py\", \"w\").write(r.text)\n",
    "\n",
    "# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry\n",
    "from notebook_utils import collect_telemetry\n",
    "\n",
    "collect_telemetry(\"deepseek-r1.ipynb\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select model for inference\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "The tutorial supports different models, you can select one from the provided options to compare the quality of open source LLM solutions.\n",
    "Model conversion and optimization is time- and memory-consuming process.  For your convenience, we provide a [collection](https://huggingface.co/collections/OpenVINO/llm-6687aaa2abca3bbcec71a9bd) of optimized models on HuggingFace hub. You can skip the model conversion step by selecting one of the available on HuggingFace hub model. If you want to reproduce optimization process locally, please unset **Use preconverted models** checkbox.\n",
    "\n",
    "* **DeepSeek-R1-Distill-Llama-8B** is a distilled model based on [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), that prioritizes high performance and advanced reasoning capabilities, particularly excelling in tasks requiring mathematical and factual precision. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more info. Note: this model can also be accelerated with [FastDraft](../../supplementary_materials/notebooks/fastdraft-deepseek/fastdraft_deepseek.ipynb).\n",
    "* **DeepSeek-R1-Distill-Qwen-1.5B** is the smallest DeepSeek-R1 distilled model based on [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B). Despite its compact size, the model demonstrates strong capabilities in solving basic mathematical tasks, at the same time its programming capabilities are limited. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more info.\n",
    "* **DeepSeek-R1-Distill-Qwen-7B** is a distilled model based on [Qwen-2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). The model demonstrates a good balance between mathematical and factual reasoning and can be less suited for complex coding tasks. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more info.\n",
    "* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more info.\n",
    "* **DeepSeek-R1-Distil-Qwen-32B** is a distilled model based on [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) that has comparable capability as OpenAI o1-mini. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) for more info. As the original model size is about 65GB, to quantize it to INT4 requires 32GB of RAM with 200GB for Swap File and another 200GB storage to save the models. The INT4 quantized model has about 16GB in size and requires 32GB of RAM when performing inference on CPU or 64GB of RAM on iGPU.\n",
    "\n",
    "[Weight compression](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html) is a technique for enhancing the efficiency of models, especially those with large memory requirements. This method reduces the model’s memory footprint, a crucial factor for Large Language Models (LLMs). We provide several options for model weight compression:\n",
    "\n",
    "* **FP16** reducing model binary size on disk using `save_model` with enabled compression weights to FP16 precision. This approach is available in OpenVINO from scratch and is the default behavior.\n",
    "* **INT8** is an 8-bit weight-only quantization provided by [NNCF](https://github.com/openvinotoolkit/nncf): This method compresses weights to an 8-bit integer data type, which balances model size reduction and accuracy, making it a versatile option for a broad range of applications.\n",
    "* **INT4** is an 4-bit weight-only quantization provided by [NNCF](https://github.com/openvinotoolkit/nncf). involves quantizing weights to an unsigned 4-bit integer symmetrically around a fixed zero point of eight (i.e., the midpoint between zero and 15). in case of **symmetric quantization** or asymmetrically with a non-fixed zero point, in case of **asymmetric quantization** respectively. Compared to INT8 compression, INT4 compression improves performance even more, but introduces a minor drop in prediction quality. INT4 it ideal for situations where speed is prioritized over an acceptable trade-off against accuracy.\n",
    "* **INT4 AWQ** is an 4-bit activation-aware weight quantization. [Activation-aware Weight Quantization](https://arxiv.org/abs/2306.00978) (AWQ) is an algorithm that tunes model weights for more accurate INT4 compression. It slightly improves generation quality of compressed LLMs, but requires significant additional time for tuning weights on a calibration dataset. We will use `wikitext-2-raw-v1/train` subset of the [Wikitext](https://huggingface.co/datasets/Salesforce/wikitext) dataset for calibration.\n",
    "* **INT4 NPU-friendly** is an 4-bit channel-wise quantization. This approach is [recommended](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai/inference-with-genai-on-npu.html) for LLM inference using NPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ced96f2a29aa4f4e9ec8a7300c371438",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from notebook_utils import device_widget\n",
    "\n",
    "device = device_widget(default=\"CPU\")\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "test_replace": {
     "get_llm_selection_widget(device=device.value)": "get_llm_selection_widget(device=device.value, default_model_idx=0)"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dc192a4ca5e41a6a4fbeda96c832bd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Box(children=(Box(children=(Label(value='Language:'), Dropdown(options=('English', 'Chinese'), value='English'…"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from llm_config import get_llm_selection_widget\n",
    "\n",
    "form, lang, model_id_widget, compression_variant, use_preconverted = get_llm_selection_widget(device=device.value)\n",
    "\n",
    "form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected model DeepSeek-R1-Distill-Qwen-1.5B with INT4 compression\n"
     ]
    }
   ],
   "source": [
    "model_configuration = model_id_widget.value\n",
    "model_id = model_id_widget.label\n",
    "print(f\"Selected model {model_id} with {compression_variant.value} compression\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert model using Optimum-CLI tool\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "🤗 [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) is the interface between the 🤗 [Transformers](https://huggingface.co/docs/transformers/index) and [Diffusers](https://huggingface.co/docs/diffusers/index) libraries and OpenVINO to accelerate end-to-end pipelines on Intel architectures. It provides ease-to-use cli interface for exporting models to [OpenVINO Intermediate Representation (IR)](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) format.\n",
    "\n",
    "<details>\n",
    "  <summary><b>Click here to read more about Optimum CLI usage</b></summary>\n",
    "\n",
    "The command bellow demonstrates basic command for model export with `optimum-cli`\n",
    "\n",
    "```\n",
    "optimum-cli export openvino --model <model_id_or_path> --task <task> <out_dir>\n",
    "```\n",
    "\n",
    "where `--model` argument is model id from HuggingFace Hub or local directory with model (saved using `.save_pretrained` method), `--task ` is one of [supported task](https://huggingface.co/docs/optimum/exporters/task_manager) that exported model should solve. For LLMs it is recommended to use `text-generation-with-past`. If model initialization requires to use remote code, `--trust-remote-code` flag additionally should be passed.\n",
    "</details>\n",
    "\n",
    "### Weights Compression using Optimum-CLI\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "You can also apply fp16, 8-bit or 4-bit weight compression on the Linear, Convolutional and Embedding layers when exporting your model with the CLI. \n",
    "<details>\n",
    "  <summary><b>Click here to read more about weights compression with Optimum CLI</b></summary>\n",
    "\n",
    "Setting `--weight-format` to respectively fp16, int8 or int4. This type of optimization allows to reduce the memory footprint and inference latency.\n",
    "By default the quantization scheme for int8/int4 will be [asymmetric](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization), to make it [symmetric](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization) you can add `--sym`.\n",
    "\n",
    "For INT4 quantization you can also specify the following arguments :\n",
    "- The `--group-size` parameter will define the group size to use for quantization, -1 it will results in per-column quantization.\n",
    "- The `--ratio` parameter controls the ratio between 4-bit and 8-bit quantization. If set to 0.9, it means that 90% of the layers will be quantized to int4 while 10% will be quantized to int8.\n",
    "\n",
    "Smaller group_size and ratio values usually improve accuracy at the sacrifice of the model size and inference latency.\n",
    "You can enable AWQ to be additionally applied during model export with INT4 precision using `--awq` flag and providing dataset name with `--dataset`parameter (e.g. `--dataset wikitext2`)\n",
    "\n",
    ">**Note**: Applying AWQ requires significant memory and time.\n",
    "\n",
    ">**Note**: It is possible that there will be no matching patterns in the model to apply AWQ, in such case it will be skipped.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⌛Found preconverted INT4 DeepSeek-R1-Distill-Qwen-1.5B. Downloading model started. It may takes some time.\n"
     ]
    },
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    },
    {
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     "output_type": "display_data"
    },
    {
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     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcce2aa4cb9a4dac9ee21db889f4d69d",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ INT4 DeepSeek-R1-Distill-Qwen-1.5B model downloaded and can be found in DeepSeek-R1-Distill-Qwen-1.5B/INT4_compressed_weights\n"
     ]
    }
   ],
   "source": [
    "from llm_config import convert_and_compress_model\n",
    "\n",
    "model_dir = convert_and_compress_model(model_id, model_configuration, compression_variant.value, use_preconverted=use_preconverted.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of model with INT4 compressed weights is 1331.98 MB\n"
     ]
    }
   ],
   "source": [
    "from llm_config import compare_model_size\n",
    "\n",
    "compare_model_size(model_dir)"
   ]
  },
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   "source": [
    "## Instantiate pipeline with OpenVINO Generate API\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[OpenVINO Generate API](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) can be used to create pipelines to run an inference with OpenVINO Runtime. \n",
    "\n",
    "Firstly we need to create a pipeline with `LLMPipeline`. `LLMPipeline` is the main object used for text generation using LLM in OpenVINO GenAI API. You can construct it straight away from the folder with the converted model. We will provide directory with model and device for `LLMPipeline`. Then we run `generate` method and get the output in text format.\n",
    "Additionally, we can configure parameters for decoding. We can create the default config with `ov_genai.GenerationConfig()`, setup parameters, and apply the updated version with `set_generation_config(config)` or put config directly to `generate()`. It's also possible to specify the needed options just as inputs in the `generate()` method, as shown below, e.g. we can add `max_new_tokens` to stop generation if a specified number of tokens is generated and the end of generation is not reached. We will discuss some of the available generation parameters more deeply later.  Generation process for long response may be time consuming, for accessing partial result as soon as it is generated without waiting when whole process finished, Streaming API can be used. Token streaming is the mode in which the generative system returns the tokens one by one as the model generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces latency, one of the most critical aspects of a smooth experience. In code below, we implement simple streamer for printing output result. For more advanced streamer example please check openvino.genai [sample](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/multinomial_causal_lm)."
   ]
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   "outputs": [
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      "Loading model from DeepSeek-R1-Distill-Qwen-1.5B/INT4_compressed_weights\n",
      "\n",
      "Input text: What is OpenVINO?\n",
      "<think>\n",
      "\n",
      "</think>\n",
      "\n",
      "OpenVINO is an open-source platform developed by Intel for the optimization, synthesis, and inference of neural networks. It is designed to enable the efficient deployment of AI models on various hardware platforms, including CPUs, GPUs, and specialized hardware like FPGAs. OpenVINO is built on top of the Intel Integrated Core Technology (ICT) platform, which provides a robust foundation for AI model optimization and acceleration.\n",
      "\n",
      "### Key Features of OpenVINO:\n",
      "1. **Neural Network Optimization**: OpenVINO optimizes AI models for better performance and energy efficiency.\n",
      "2. **Synthesis and Inference**: It synthesizes"
     ]
    }
   ],
   "source": [
    "import openvino_genai as ov_genai\n",
    "import sys\n",
    "\n",
    "print(f\"Loading model from {model_dir}\\n\")\n",
    "\n",
    "\n",
    "pipe = ov_genai.LLMPipeline(str(model_dir), device.value)\n",
    "if \"genai_chat_template\" in model_configuration:\n",
    "    pipe.get_tokenizer().set_chat_template(model_configuration[\"genai_chat_template\"])\n",
    "\n",
    "generation_config = ov_genai.GenerationConfig()\n",
    "generation_config.max_new_tokens = 128\n",
    "\n",
    "\n",
    "def streamer(subword):\n",
    "    print(subword, end=\"\", flush=True)\n",
    "    sys.stdout.flush()\n",
    "    # Return flag corresponds whether generation should be stopped.\n",
    "    # False means continue generation.\n",
    "    return False\n",
    "\n",
    "\n",
    "input_prompt = \"What is OpenVINO?\"\n",
    "print(f\"Input text: {input_prompt}\")\n",
    "result = pipe.generate(input_prompt, generation_config, streamer)"
   ]
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    "## Run Chatbot\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Now, when model created, we can setup Chatbot interface using [Gradio](https://www.gradio.app/).\n",
    "\n",
    "<details>\n",
    "  <summary><b>Click here to see how pipeline works</b></summary>\n",
    "\n",
    "The diagram below illustrates how the chatbot pipeline works\n",
    "\n",
    "![llm_diagram](https://github.com/user-attachments/assets/9c9b56e1-01a6-48d8-aa46-222a88e25066)\n",
    "\n",
    "As you can see, user input question passed via tokenizer to apply chat-specific formatting (chat template) and turn the provided string into the numeric format. [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) are used for these purposes inside `LLMPipeline`. You can find more detailed info about  tokenization theory and OpenVINO Tokenizers in this [tutorial](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/openvino-tokenizers/openvino-tokenizers.ipynb). Then tokenized input passed to LLM for making prediction of next token probability. The way the next token will be selected over predicted probabilities is driven by the selected decoding methodology.  You can find more information about the most popular decoding methods in this [blog](https://huggingface.co/blog/how-to-generate). The sampler's goal is to select the next token id is driven by generation configuration. Next, we apply stop generation condition to check the generation is finished or not (e.g. if we reached the maximum new generated tokens or the next token id equals to end of the generation).  If the end of the generation is not reached, then new generated token id is used as the next iteration input, and the generation cycle repeats until the condition is not met. When stop generation criteria are met, then OpenVINO Detokenizer decodes generated token ids to text answer. \n",
    "\n",
    "The difference between chatbot and instruction-following pipelines is that the model should have \"memory\" to find correct answers on the chain of connected questions. OpenVINO GenAI uses `KVCache` representation for maintain a history of conversation. By default, `LLMPipeline` resets `KVCache` after each `generate` call. To keep conversational history, we should move LLMPipeline to chat mode using `start_chat()` method.\n",
    "\n",
    "More info about OpenVINO LLM inference can be found in [LLM Inference Guide](https://docs.openvino.ai/2025/openvino-workflow-generative.html)\n",
    "</details>"
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     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/gradio/components/chatbot.py:285: UserWarning: You have not specified a value for the `type` parameter. Defaulting to the 'tuples' format for chatbot messages, but this is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style dictionaries with 'role' and 'content' keys.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
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       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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     "text": [
      "Keyboard interruption in main thread... closing server.\n"
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    "if not Path(\"gradio_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/deepseek-r1/gradio_helper.py\")\n",
    "    open(\"gradio_helper_genai.py\", \"w\").write(r.text)\n",
    "\n",
    "from gradio_helper import make_demo\n",
    "\n",
    "demo = make_demo(pipe, model_configuration, model_id, lang.value, device.value == \"NPU\")\n",
    "\n",
    "try:\n",
    "    demo.launch(debug=True)\n",
    "except Exception:\n",
    "    demo.launch(debug=True, share=True)\n",
    "# If you are launching remotely, specify server_name and server_port\n",
    "# EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`\n",
    "# To learn more please refer to the Gradio docs: https://gradio.app/docs/"
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